1 code implementation • COLING 2022 • Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu
Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing.
no code implementations • 22 Feb 2024 • Yu Gu, Yiheng Shu, Hao Yu, Xiao Liu, Yuxiao Dong, Jie Tang, Jayanth Srinivasa, Hugo Latapie, Yu Su
The applications of large language models (LLMs) have expanded well beyond the confines of text processing, signaling a new era where LLMs are envisioned as generalist language agents capable of operating within complex real-world environments.
1 code implementation • 15 Sep 2023 • Yiheng Shu, Zhiwei Yu
Language models (LMs) have already demonstrated remarkable abilities in understanding and generating both natural and formal language.
1 code implementation • 13 Jun 2023 • Xiang Huang, Sitao Cheng, Yiheng Shu, Yuheng Bao, Yuzhong Qu
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA.
2 code implementations • 24 Oct 2022 • Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
1 code implementation • 30 Apr 2019 • Hui Fang, Danning Zhang, Yiheng Shu, Guibing Guo
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones.